arXiv subjects

Ali Jafarizadeh

Publications and source records attributed to Ali Jafarizadeh.

5 recordsLinked to original sources

arXiv2026

Artificial intelligence applications in Parkinson's disease via retinal imaging

Parkinson's disease (PD) is projected to increase substantially due to population aging, making early diagnosis increasingly important, as timely detection may delay progression and reduce long-term complications. Retinal microvasculature has emerged as a promising anatomical biomarker of neurodegeneration, and when combined with artificial intelligence AI, retinal imaging may provide an advanced, noninvasive, and cost-effective screening strategy for PD. This study evaluated the evidence from the past 35 years regarding the capability of AI to detect early PD-related changes in retinal vascular structure. Five electronic databases including PubMed, Web of Science, Scopus, ScienceDirect, and ProQuest were systematically searched from January 1990 to January 2025. In addition, Annals of Neurology and Frontiers in Neuroscience were hand-searched, and the reference lists of included studies were screened for additional eligible publications. Nineteen studies met the inclusion criteria. Three principal diagnostic AI tasks were identified, including disease classification, retinal vessel segmentation, and PD risk stratification. The best-performing models were ShAMBi-LSTM on the Drishti dataset with 97.2 percent accuracy, 99.5 percent precision, 96.9 percent sensitivity, and an F1 score of 0.981 for classification, nnU-Net with 99.7 percent accuracy, 98.7 percent precision, 98.9 percent sensitivity, 99.8 percent specificity, and a Dice score of 98.9 percent for segmentation, and AlexNet for risk prediction with area under the curve values of 0.77, 0.68, and 0.73 across datasets. Overall, application of AI algorithms to retinal vasculature for detecting early signs of PD and predicting disease severity suggests that integration of AI with retinal biomarkers holds substantial potential for earlier and more accurate detection compared with traditional clinical evaluation alone.

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arXiv2024

Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade

Background: Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods - in particular, deep learning (DL) - has been on the rise lately for the analysis of different CVD-related topics. The use of fundus images and optical coherence tomography angiography (OCTA) in the diagnosis of retinal diseases has also been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with non-invasive retinal scanning. There is great potential to reduce the number of cardiovascular events and the financial strain on healthcare systems by utilizing AI-assisted early detection and prediction of cardiovascular diseases on a large scale. Method: A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and artificial intelligence. Results: The study included 87 English-language publications selected for relevance, and additional references were considered. This paper provides an overview of the recent developments and difficulties in using artificial intelligence and retinal imaging to diagnose cardiovascular diseases. It provides insights for further exploration in this field. Conclusion: Researchers are trying to develop precise disease prognosis patterns in response to the aging population and the growing global burden of CVD. AI and deep learning are revolutionizing healthcare by potentially diagnosing multiple CVDs from a single retinal image. However, swifter adoption of these technologies in healthcare systems is required.

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arXiv2024

Advancements in Radiomics and Artificial Intelligence for Thyroid Cancer Diagnosis

Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. The application of AI and radiomics to thyroid cancer diagnosis is examined in this review. A review of multiple databases was conducted in compliance with PRISMA guidelines until October 2023. A combination of keywords led to the discovery of an English academic publication on thyroid cancer and related subjects. 267 papers were returned from the original search after 109 duplicates were removed. Relevant studies were selected according to predetermined criteria after 124 articles were eliminated based on an examination of their abstract and title. After the comprehensive analysis, an additional six studies were excluded. Among the 28 included studies, radiomics analysis, which incorporates ultrasound (US) images, demonstrated its effectiveness in diagnosing thyroid cancer. Various results were noted, some of the studies presenting new strategies that outperformed the status quo. The literature has emphasized various challenges faced by AI models, including interpretability issues, dataset constraints, and operator dependence. The synthesized findings of the 28 included studies mentioned the need for standardization efforts and prospective multicenter studies to address these concerns. Furthermore, approaches to overcome these obstacles were identified, such as advances in explainable AI technology and personalized medicine techniques. The review focuses on how AI and radiomics could transform the diagnosis and treatment of thyroid cancer. Despite challenges, future research on multidisciplinary cooperation, clinical applicability validation, and algorithm improvement holds the potential to improve patient outcomes and diagnostic precision in the treatment of thyroid cancer.

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arXiv2024

Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina

Diabetes mellitus (DM) predisposes patients to vascular complications. Retinal images and vasculature reflect the body's micro- and macrovascular health. They can be used to diagnose DM complications, including diabetic retinopathy (DR), neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as forecast the risk of cardiovascular events. Artificial intelligence (AI)-enabled systems developed for high-throughput detection of DR using digitized retinal images have become clinically adopted. Beyond DR screening, AI integration also holds immense potential to address challenges associated with the holistic care of the patient with DM. In this work, we aim to comprehensively review the literature for studies on AI applications based on retinal images related to DM diagnosis, prognostication, and management. We will describe the findings of holistic AI-assisted diabetes care, including but not limited to DR screening, and discuss barriers to implementing such systems, including issues concerning ethics, data privacy, equitable access, and explainability. With the ability to evaluate the patient's health status vis a vis DM complication as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM.

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arXiv2024

Current and future roles of artificial intelligence in retinopathy of prematurity

Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) models face challenges like accuracy and overfitting. Recent advancements in deep learning (DL), especially convolutional neural networks (CNNs), have significantly improved ROP detection and classification. The i-ROP deep learning (i-ROP-DL) system also shows promise in detecting plus disease, offering reliable ROP diagnosis potential. This research comprehensively examines the contemporary progress and challenges associated with using retinal imaging and artificial intelligence (AI) to detect ROP, offering valuable insights that can guide further investigation in this domain. Based on 89 original studies in this field (out of 1487 studies that were comprehensively reviewed), we concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions. AI holds great promise for improving ROP management. This review explores AI's potential in ROP detection, classification, diagnosis, and prognosis.

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